General

Column

Target



  • Qual área de pesquisa é emergente?



  • Qual pesquisador contratar?



  • Qual patente comprar?



Growth

Networks

Groups Attributes

Column

Shelf Life



  • Shelf Life

O período total de tempo entre a data de fabricação, embalagem, transporte e armazenagem, que ainda permanece utilizável para o consumo pelo usuário final.



  • Shelf Life Research

  • 13,516 Registers

  • 12.9% Growth Rate

  • 5.6 Years Doubling Time



  • Scopus Research

  • 52,000,000 Registers

  • 4.13% Growth Rate

  • 17 Years Doubling Time

Segmented Growth

Groups Growth

Groups Description

g01

Aplicação shiny

g02

Grupo g05 - Embalagens ativas e nanotecnologia

Column

Authors

Author h_index g_index m_index TC NP PY_start
LI X 16 28 0.8888889 948 80 2003
LIU Y 14 27 1.2727273 818 60 2010
WANG L 9 19 0.6923077 387 38 2008
ZHANG H 12 20 1.0000000 525 57 2009
LI L 12 20 0.9230769 520 52 2008
LIN L 6 13 0.8571429 187 14 2014
VICENTE AA 18 20 1.3846154 1463 20 2008
ZHAO Y 15 34 0.9375000 1197 47 2005
LI Y 21 38 1.0000000 1526 72 2000
WANG X 14 28 1.4000000 859 61 2011
WANG Y 18 33 1.1250000 1199 78 2005
CUI H 6 12 0.5454545 164 14 2010
LACROIX M 18 28 0.7826087 1182 28 1998
MINH NP 2 2 0.5000000 14 42 2017
PREZ-GAGO MB 12 16 0.9230769 548 16 2008
CERQUEIRA MA 11 11 0.9166667 759 11 2009
CONTE A 22 38 1.6923077 1525 57 2008
DEL NOBILE MA 25 43 1.0416667 1991 69 1997
HU Y 10 18 0.9090909 354 22 2010
JAFARI SM 10 17 1.4285714 396 17 2014
PALOU L 11 16 0.9166667 481 16 2009
RHIM JW 9 10 1.1250000 923 10 2013
CHEN H 12 19 0.9230769 410 32 2008
GAVARA R 13 17 0.6842105 1162 17 2002
LI J 18 31 1.0588235 1060 72 2004
LI W 8 17 0.7272727 294 21 2010
SONG KB 13 22 0.7647059 549 22 2004
ZHANG Y 17 27 1.5454545 842 77 2010
ALMASI H 5 8 0.7142857 78 8 2014
JIANG Y 14 33 0.7000000 1168 33 2001
KERRY JP 23 41 1.2105263 1728 46 2002
NERN C 12 19 0.7500000 1191 19 2005
QIN W 4 8 1.0000000 64 10 2017
SALMIERI S 8 12 0.6666667 240 12 2009
WANG J 13 26 0.5200000 753 60 1996
WANG S 13 23 1.1818182 552 30 2010
HERNNDEZ-MUOZ P 10 11 0.5263158 706 11 2002
LI C 7 11 0.6363636 142 22 2010
LI S 4 10 0.4444444 107 20 2012
LIU L 10 14 0.9090909 227 28 2010
SHAHBAZI Y 5 12 1.0000000 144 14 2016
SOTHORNVIT R 8 12 0.6153846 358 12 2008
WANG H 15 26 1.3636364 760 59 2010
WU Y 7 15 0.7000000 239 21 2011
ZHANG C 8 12 1.1428571 170 27 2014
BIGGER SW 5 5 0.5000000 232 5 2011
CHEN J 10 21 0.5263158 507 42 2002
DUTTA J 5 5 0.4166667 1092 5 2009
EHSANI A 6 14 0.7500000 217 15 2013
HASHEMI M 8 15 1.1428571 249 16 2014
HUANG J 7 15 0.5000000 251 18 2007
KUMAR S 8 14 0.5714286 240 26 2007
LAGARON JM 7 7 0.6363636 231 7 2010
LIU X 11 20 1.3750000 454 37 2013
MILTZ J 6 7 0.2068966 268 7 1992
MOHEBBI M 7 10 0.7777778 143 10 2012
QIN Y 6 7 1.0000000 90 7 2015
VITTORIA V 2 5 0.2222222 35 5 2012
VU KD 4 6 0.4444444 185 6 2012
WU C 8 13 0.5714286 257 13 2007
XIE J 8 11 0.7272727 139 29 2010
YANG H 9 16 1.2857143 275 25 2014
ZHANG M 15 24 0.8823529 697 56 2004
ZHANG R 8 13 1.3333333 183 15 2015
ZHANG X 14 25 0.7777778 685 52 2003
AHMED S 4 5 0.6666667 32 8 2015
AMINZARE M 4 8 0.6666667 67 9 2015
BUGATTI V 2 5 0.3333333 28 6 2015
CABEDO L 3 4 0.1875000 187 4 2005
CRAN MJ 4 4 0.4000000 189 4 2011
DONS F 4 5 0.4000000 237 5 2011
DUBEY NK 14 21 1.0000000 1187 21 2007
FERRARI G 8 9 0.6666667 353 9 2009
HAMISHEHKAR H 6 6 1.5000000 95 6 2017
JIANG S 4 8 0.4444444 71 10 2012
JIMNEZ A 4 10 0.4000000 534 10 2011
KANMANI P 5 7 0.5555556 636 7 2012
KUMAR A 11 28 0.3666667 814 29 1991
KUREK M 4 7 0.4000000 123 7 2011
LAGARN JM 5 6 0.2941176 494 6 2004
LI B 5 9 0.3846154 99 15 2008
LI H 6 10 0.7500000 112 23 2013
LI N 8 12 0.8000000 372 12 2011
LIM LT 7 8 0.5833333 287 8 2009
LIU H 6 10 0.3333333 122 19 2003
LIU J 8 16 0.6666667 274 31 2009
LIU S 8 14 0.7272727 222 20 2010
LPEZ-CARBALLO G 5 5 0.5555556 141 5 2012
MA Y 10 24 0.4761905 606 26 2000
MCHUGH TH 8 8 0.5333333 594 8 2006
MORADI M 4 8 0.4000000 110 8 2011
RAEISI M 4 9 0.6666667 145 9 2015
RAMOS M 10 14 0.4761905 447 14 2000
SADEGHI K 3 4 1.5000000 17 4 2019
SEO J 3 3 1.0000000 13 4 2018
SHANKAR S 3 4 0.5000000 141 4 2015
SINGH P 16 28 0.9411765 929 28 2004
SINGH S 9 19 0.4500000 392 25 2001
SNCHEZ G 7 7 0.7000000 150 7 2011
SOUZA BWS 5 5 0.4545455 322 5 2010
SUN Y 6 9 0.8571429 117 22 2014
TAJIK H 8 17 0.6153846 300 17 2008
TALIB RA 3 4 0.4285714 51 4 2014
TAWAKKAL ISMA 3 4 0.4285714 125 4 2014
TEIXEIRA JA 7 7 0.5384615 484 7 2008
UYAR T 6 6 1.2000000 228 6 2016
WANG Q 9 18 0.8181818 340 28 2010
WU W 4 10 0.5714286 111 11 2014
XU J 7 9 0.7000000 105 16 2011
YANG W 7 11 0.5000000 128 19 2007
ZHONG Y 4 7 0.3636364 261 7 2010
AGUILAR CN 3 4 0.2142857 90 4 2007
ALMAJANO MP 4 4 0.6666667 21 5 2015
ANANTHANARAYAN L 4 5 0.8000000 30 5 2016
AVENA-BUSTILLOS RJ 5 5 0.3333333 469 5 2006
BANERJEE S 4 5 0.3333333 57 5 2009
BHANDARI B 8 14 0.5333333 260 14 2006
CAO W 2 3 0.5000000 9 3 2017
CHELLIAH R 3 6 0.6000000 50 6 2016
CHEN M 6 12 1.0000000 156 16 2015
CHEN S 8 19 0.7272727 363 20 2010
CHEN X 11 22 0.5500000 493 27 2001
CHIRALT A 11 13 0.5500000 707 13 2001
CHOUDHARY R 3 3 0.7500000 52 3 2017
COSTA C 9 14 0.8181818 505 14 2010
CRUZ-ROMERO MC 7 9 0.7777778 202 9 2012
CUMMINS E 5 6 0.8333333 123 6 2015
DAS S 5 8 0.5000000 78 14 2011
DE LA FUENTE B 2 3 0.5000000 12 3 2017
DEBEAUFORT F 6 7 0.4285714 166 7 2007
DEGRAEVE P 4 4 0.3333333 134 4 2009
DI MAIO L 3 5 0.2000000 40 5 2006
DWIVEDY AK 6 8 0.6666667 109 8 2012
ERCOLINI D 8 8 0.4705882 552 8 2004
FARHOODI M 2 5 0.4000000 47 5 2016
GALOTTO MJ 4 6 0.2500000 212 6 2005
GAO H 7 15 0.5384615 232 20 2008
GAO X 3 5 0.1875000 51 5 2005
GARRIGS MC 4 6 0.5714286 197 6 2014
GIANNAKAS A 2 2 1.0000000 5 4 2019
GNIEWOSZ M 6 9 0.6000000 120 9 2011
GONTARD N 10 15 0.4000000 683 15 1996
GORRASI G 2 5 0.4000000 27 5 2016
GUARDA A 4 6 0.2500000 212 6 2005
GUO X 8 14 0.8000000 200 15 2011
GUO Y 8 18 1.1428571 359 23 2014
HAGHIGHI H 3 3 1.0000000 36 3 2018
HE J 2 4 0.3333333 45 4 2015
HE L 3 6 0.3750000 41 7 2013
HEJAZI J 1 3 0.2000000 30 3 2016
HONG YH 3 5 0.2307692 83 5 2008
HU X 9 17 1.0000000 314 24 2012
INCARNATO L 3 5 0.2000000 40 5 2006
JAMRZ E 2 3 0.6666667 11 4 2018
JOOYANDEH H 2 4 0.2000000 29 4 2011
KANG H 3 6 0.4285714 44 8 2014
KARBOWIAK T 5 6 0.3571429 133 6 2007
KHAN A 2 4 0.2857143 131 4 2014
KHAN MKI 3 5 0.3333333 47 5 2012
KOHLI P 3 3 0.7500000 52 3 2017
KUMAR N 2 4 0.1250000 19 5 2005
KUMAR P 6 11 0.3157895 130 12 2002
LA STORIA A 7 7 0.4117647 531 7 2004
LAN W 2 5 0.5000000 32 8 2017
LEE H 4 8 0.5000000 104 8 2013
LI D 11 24 0.8461538 602 32 2008
LI T 6 16 0.6666667 273 20 2012
LIANG M 2 4 0.5000000 19 5 2017
LICCIARDELLO F 9 12 0.5625000 231 12 2005
LIU D 6 15 0.5000000 228 19 2009
LPEZ-MALO A 3 4 0.2727273 175 4 2010
MAFTOONAZAD N 6 8 0.3750000 348 8 2005
MARTINS JT 5 5 0.4545455 589 5 2010
MAURIELLO G 6 6 0.3529412 506 6 2004
MEDEIROS EAA 3 3 0.3000000 123 3 2011
MIN SC 3 4 0.3750000 91 4 2013
MOREIRA MDR 4 5 0.4000000 177 5 2011
MORTAZAVI SA 5 12 0.4545455 206 12 2010
MURIEL-GALET V 4 4 0.4444444 109 4 2012
NEVREZ-MOORILLN GV 4 4 0.3636364 217 4 2010
OTHMAN SH 3 3 0.4285714 51 3 2014
PALOU E 3 4 0.2727273 175 4 2010
PAPKOVSKY DB 7 10 0.3684211 217 10 2002
PRAKASH B 13 17 1.1818182 912 17 2010
PULVIRENTI A 3 5 0.1578947 36 5 2002
QUINTANAR-GUERRERO D 4 4 0.5000000 124 4 2013
RIBEIRO-SANTOS R 4 6 1.0000000 92 6 2017
SADIQ MB 2 3 0.6666667 24 3 2018
SAHARI MA 6 8 0.3529412 154 8 2004
SAMEEN DE 1 1 1.0000000 1 3 2020
SANCHES-SILVA A 6 8 0.5454545 214 8 2010
SEBTI I 3 3 0.2500000 104 3 2009
SHAVISI N 4 8 0.8000000 80 10 2016
SOBRAL PJA 6 8 0.3750000 372 8 2005
SRIVASTAVA AK 2 4 0.5000000 70 4 2017
THANAKKASARANEE S 2 2 0.6666667 7 3 2018
TORRES-GINER S 4 4 1.0000000 76 4 2017
VERTUCCIO L 2 5 0.4000000 27 5 2016
VILLANI F 8 8 0.4705882 590 8 2004
WANG Z 8 14 0.6153846 235 30 2008
H-index is defined as the maximum value of h such that the given author/journal has published h papers that have each been cited at least h times.
G-index is an improvement of the h-index. If this set is ranked in decreasing order of the number of citations that they received, the g-index is the (unique) largest number such that the top g articles received (together) at least g2 citations.
M-Index is the H-index divided by the number of years that a scientist has been active.

Authors Production

Authors Collaboration

Column

Countries

treemap here

Countries Network

rede de colaboração entre países

Papers

principais trabalhos aqui

Conclusions

Column

Column

Informations

A4F Group

Roney Fraga Souza
Demais Pesquisadores
---
title: "A4F - Shelf Life"
output: 
  flexdashboard::flex_dashboard:
    navbar:
      - { title: "Research", href: "http://roneyfraga.com/dash/2020_A4F", align: right }
      - { title: "People", href: "http://roneyfraga.com/dash/2020_A4F/#people", align: right }
      - { title: "Patent", href: "http://roneyfraga.com/dash/2020_A4F/#patent", align: right }
      - { title: "About", href: "http://roneyfraga.com/", align: right }
    social: [ "menu" ]
    source_code: "embed"
    theme: bootstrap #yeti #lumen
    logo: img/logo.png
runtime: shiny
---




```{r setup, include=FALSE}
options(scipen=999)
library(rmarkdown)
library(flexdashboard)
library(pipeR)
library(tidyverse)
library(rio)
library(ggraph)
library(tidygraph)
library(DT)
library(visNetwork)
library(igraph)
library(highcharter)
library(htmlwidgets)
library(printr)
library(shiny)
library(kableExtra)
```


# General 


Column {data-width=500 .tabset}
-------------------------------------


### Target



- Qual área de pesquisa é emergente?



- Qual pesquisador contratar?



- Qual patente comprar?



### Growth ```{r} import('data/growth_shelf_life.rds') %>>% (. -> d2) hchart(d2, "column", hcaes(x = Year, y = Publications), name = "Publications", showInLegend = TRUE) %>>% hc_add_series(d2, "line", hcaes(x = Year, y = predicted), name = "Predicted", showInLegend = TRUE) %>>% hc_add_theme(hc_theme_elementary()) %>>% hc_navigator( enabled = TRUE) %>>% hc_exporting( enabled = TRUE, filename='groups_growth') ``` ### Networks ```{r,dpi=150} knitr::include_url('inseption_html/revealjs_defaults.html', height='90%') ``` ### Groups Attributes ```{r} import('data/groups_attributes.rds') %>>% datatable(extensions = 'Buttons', rownames=F, options = list(dom = 'Bfrtip', pageLength = 13, buttons = list(list( extend='collection', buttons = list(list(extend='csv',filename='data'), list(extend='excel',filename='data')), text='Download')))) %>>% formatRound('GrowthRateYear',1) ``` Column {data-width=500 .tabset} ------------------------------------- ### Shelf Life

- Shelf Life

O período total de tempo entre a data de fabricação, embalagem, transporte e armazenagem, que ainda permanece utilizável para o consumo pelo usuário final.



- Shelf Life Research

- 13,516 Registers \n

- 12.9% Growth Rate \n

- 5.6 Years Doubling Time \n



- Scopus Research

- 52,000,000 Registers \n

- 4.13% Growth Rate \n

- 17 Years Doubling Time \n

> ### Segmented Growth ```{r, out.width='75%'} import('data/segmented_growth.rds') %>>% (. -> d2) hchart(d2, "line", hcaes(x = Year, y = ln_Publications), name = "Publications", showInLegend = TRUE, fillOpacity = 0.2) %>>% hc_add_series(d2, "line", hcaes(x = Year, y = est), name = "Segmented Regression", showInLegend = TRUE, fillOpacity = 0.2) %>>% hc_add_theme(hc_theme_elementary()) %>>% hc_navigator( enabled = TRUE) %>>% hc_exporting( enabled = TRUE, filename='segmented_growth') %>>% hc_xAxis( plotBands = list( list( from = 1986, to = 1986, color = "#330000" ), list( from = 1992, to = 1992, color = "#330000" ), list( from = 2004, to = 2004, color = "#330000" ) )) ``` ### Groups Growth ```{r} import('data/groups_growth.rds') %>>% (. -> groups_growth) hchart(groups_growth, "line", hcaes(x = Year, y = Publications, group = Group), fillOpacity = 0.2) %>>% hc_add_theme(hc_theme_elementary()) %>>% hc_navigator( enabled = TRUE) %>>% hc_exporting( enabled = TRUE, filename='groups_growth') ``` ### Groups Description ```{r} import('data/groups_description.txt') %>>% datatable(extensions = 'Buttons', rownames=F, options = list(dom = 'Bfrtip', pageLength = 13, buttons = list(list( extend='collection', buttons = list(list(extend='csv',filename='data'), list(extend='excel',filename='data')), text='Download')))) ``` # g01 {data-navmenu="Groups"} ### Aplicação `shiny` ```{r } numericInput("obs", label = "Number of carbs:", 2, min=1, max=8) show_mtcars <- reactive({ mtcars %>>% dplyr::filter(carb == input$obs) }) renderTable(show_mtcars()) ``` # g02 {data-navmenu="Groups"}

Grupo g05 - Embalagens ativas e nanotecnologia

Column {data-width=500 .tabset} ------------------------------------- ### Authors ```{r} import('data/indices_g05.rds') %>>% (kable(.,'html')) %>>% kable_styling() %>>% scroll_box(width = "100%", height = "660px") ```
H-index is defined as the maximum value of h such that the given author/journal has published h papers that have each been cited at least h times.
G-index is an improvement of the h-index. If this set is ranked in decreasing order of the number of citations that they received, the g-index is the (unique) largest number such that the top g articles received (together) at least g2 citations.
M-Index is the H-index divided by the number of years that a scientist has been active.
### Authors Production ```{r} # import('data/authorProd_g05.rds') %>>% (. -> authorProd) # authorProd$graph knitr::include_graphics('img/top_authors_g05.png') ``` ### Authors Collaboration ```{r} knitr::include_graphics('img/authors_collaboration_g05.png') ``` Column {data-width=500 .tabset} ------------------------------------- ### Countries `treemap here` ### Countries Network `rede de colaboração entre países` ### Papers `principais trabalhos aqui` # Conclusions Column {data-width=700} ------------------------------------- ```{r,dpi=250} knitr::include_url('inseption_html/revealjs_defaults.html', height='95%') ``` Column {data-width=300} ------------------------------------- ### Informations

A4F Group

Roney Fraga Souza
Demais Pesquisadores
# People {.hidden} Adicionar os grandes números do Lattes. # Patent {.hidden} Em construção.